1. A Universal SOC Model Prof. Lei He Electric Engineering Department, UCLA http://eda.ee.ucla.edu LHE@ee.ucla.edu 2010. 7

2. 2 of 34  Motivation  Existing Work  Proposed Approach  Experimental Results  Conclusions Outline

3. 3 of 34  Demand for Rechargeable Batteries Portable Products such as laptops and cell phones Electric Vehicles and smart grid  Battery Management System To improve the efficiency of charging and discharging To prolong life span To satisfy the real-time requirement of power  Key Models: SOC, SOH, and SOP SOC = State of Charge, energy remaining in a battery SOH, SOP = State of health, state of power Motivation

4. 4 of 34  Battery cell is a two-terminal “black box”  Battery ages (more than NBTI)  SOC needs to be monitored real-time and life-long  SOC depends on temperature (like leakage)  SOC needs to be measured for each cell  Measurement method should not use complicated circuits and systems  It has to be reliable against rare events  It needs to be tolerant to abuse to certain degree  …. Why It is Challenging

5. 5 of 34  Motivation  Existing Work  Proposed Approach  Experimental Results  Conclusions Outline

6. 6 of 34  Coulomb-Counting Based Estimation  SoC is an integration function of time.  However, error will be accumulated over time.  Voltage-Based Estimation  Bijection between SoC and Open-Circuit Voltage (OCV)  Then how to obtain OCV from the terminal voltage and current? Existing Work Source: P. Moss, G. Au, E. Plichta, and J. P. Zheng, “An electrical circuit for modeling the dynamic response of li-ion polymer batteries,” Journal of The Electrochemical Society, 2008.

7. 7 of 34  A variety of methods  Weighted Recursive Least Square Regression  Adaptive Digital Filter  Extended Kalman Filter  Radial Basis Function Neural Network ………… Simplified circuit models applied to reduced the complexity Existing Voltage-based SOC

8. 8 of 34 Parameters need to be tuned for different battery types and individual battery cells Regression for Existing Models predetermined to be decided Source: H. Asai, H. Ashizawa, D. Yumoto, and H. Nakam, “Application of an Adaptive Digital Filter for Estimation of Internal Battery Conditions,” in SAE World Congress, 2005. Source: M. Verbrugge, D. Frisch, and B. Koch, “Adaptive Energy Management of Electric and Hybrid Electric Vehicles,” Journal of Power Sources, 2005.

9. 9 of 34  Motivation  Existing Work  Proposed Approach  Experimental Results  Conclusion Outline

10. 10 of 34  Problem of Existing Work Models are developed for specific types of batteries  Characteristics of Proposed Approach Using linear system analysis but without a circuit model Low complexity for real-time battery management  The Only Assumption Used in Proposed Approach Within the short observing time window, a battery is treated as a time-invariant linear system and the SoC and accordingly the OCV is treated as constants. Proposed Approach

11. 11 of 34 Initial Time Window

12. 12 of 34 Initial Time Window Current Load Voltage Response = OCV f + Zero-State Response Unit Step Function Convolute with f (t) which satisfies in the window. + Impulse Response VfVf = OCV ufuf Impulse Stimulation unknown region

13. 13 of 34 Next Window

14. 14 of 34 Voltage ResponseCurrent Stimulation Following Windows Current Stimulation Unit Step Function VfVf ufuf Impulse Stimulation Impulse Response History Influence convolution

15. 15 of 34 Special Situations  Case I:  u f also converges to zero as t approaches infinity.  I.e., u f (t) = 0 for t > 0.  Then, the terminal current is constant and the battery becomes a pure resistance network. Case II:  The first sample of terminal current in the window is close to 0.  Then move the window to the next sample as the starting point.  The extreme case is that the sampled current is keeps 0  battery in open-circuit state.  battery in open-circuit state.

16. 16 of 34  Motivation  Existing Works  Proposed Approach  Experimental Results  Conclusion Outline

17. 17 of 34  Verified via dualfoil5, a popular battery simulator Simulation input: current waveform, load or power. Battery materials: a library containing common materials. Simulation output: SOC, OCV, terminal voltage and current.  Implementation Environment MATLAB 7.01 running on a dual-core Pentium 4 CPU at a 1.73GHz clock frequency. Experimental Settings

18. 18 of 34  The extracted SoC fits well with the simulated data (labeled as simulated) for different current profiles. Accuracy

19. 19 of 34  Error within 4% for different materials for active positive material / electrolyte / negative positive material of batteries (Labeled).  For each type of battery Only a discharge from fully-charged to empty-charged is conducted to build up the bijection between OCV and SoC. No other tuning is needed. Universality

20. 20 of 34  The algorithm converges quickly to the correct SoC despite an upset on SoC. Robustness

21. 21 of 34  A Universal State-of-Charge Algorithm for Batteries A simple yet accurate algorithm to calculate open-circuit voltage (OCV) based on terminal voltage and current of the battery. Only linear system analysis used without any circuit model and hence universality to discharge current profile and any battery types without modification. Experiments showing less than 4% SoC error compared to detailed battery simulation.  Future work Fixed point and FPGA implementation Hardware in loop testing Conclusions